Deep learning has accelerated the progress in many computer vision problems, including various applications in medical imaging [1]}, [2]}, [3]}, [4]}. Most deployed architectures are based on convolutional neural networks (CNNs) which learn large amounts of convolution filters to transform inputs into meaningful feature spaces that can be further processed.
Since the same CNN architectures can learn many computer vision tasks and work on diverse image domains, the question naturally arises whether there are shared similarities in the learned convolution filters.
